{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pyplot Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本绘图\n",
    "首先生成数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#平均采样，50个点\n",
    "x=np.linspace(-3,3,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.        , -4.75510204, -4.51020408, -4.26530612, -4.02040816,\n",
       "       -3.7755102 , -3.53061224, -3.28571429, -3.04081633, -2.79591837,\n",
       "       -2.55102041, -2.30612245, -2.06122449, -1.81632653, -1.57142857,\n",
       "       -1.32653061, -1.08163265, -0.83673469, -0.59183673, -0.34693878,\n",
       "       -0.10204082,  0.14285714,  0.3877551 ,  0.63265306,  0.87755102,\n",
       "        1.12244898,  1.36734694,  1.6122449 ,  1.85714286,  2.10204082,\n",
       "        2.34693878,  2.59183673,  2.83673469,  3.08163265,  3.32653061,\n",
       "        3.57142857,  3.81632653,  4.06122449,  4.30612245,  4.55102041,\n",
       "        4.79591837,  5.04081633,  5.28571429,  5.53061224,  5.7755102 ,\n",
       "        6.02040816,  6.26530612,  6.51020408,  6.75510204,  7.        ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1=2*x+1\n",
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2=x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.00000000e+00, 8.28029988e+00, 7.59058726e+00, 6.93086214e+00,\n",
       "       6.30112453e+00, 5.70137443e+00, 5.13161183e+00, 4.59183673e+00,\n",
       "       4.08204915e+00, 3.60224906e+00, 3.15243648e+00, 2.73261141e+00,\n",
       "       2.34277384e+00, 1.98292378e+00, 1.65306122e+00, 1.35318617e+00,\n",
       "       1.08329863e+00, 8.43398584e-01, 6.33486047e-01, 4.53561016e-01,\n",
       "       3.03623490e-01, 1.83673469e-01, 9.37109538e-02, 3.37359434e-02,\n",
       "       3.74843815e-03, 3.74843815e-03, 3.37359434e-02, 9.37109538e-02,\n",
       "       1.83673469e-01, 3.03623490e-01, 4.53561016e-01, 6.33486047e-01,\n",
       "       8.43398584e-01, 1.08329863e+00, 1.35318617e+00, 1.65306122e+00,\n",
       "       1.98292378e+00, 2.34277384e+00, 2.73261141e+00, 3.15243648e+00,\n",
       "       3.60224906e+00, 4.08204915e+00, 4.59183673e+00, 5.13161183e+00,\n",
       "       5.70137443e+00, 6.30112453e+00, 6.93086214e+00, 7.59058726e+00,\n",
       "       8.28029988e+00, 9.00000000e+00])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116754eb8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146dec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1167fe668>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116728710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116885668>,\n",
       " <matplotlib.lines.Line2D at 0x1168857b8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzt3XecVPW9//HXZwudRcCVtiAINqRYEHsiiooVjQ1jiZoEY8yNyTWJMf7uNddc00yMJpZc7LEbsffeIwrKLtUCgjtLW8rM7lK2fn9/fIeISFmYs3Nmznk/Hw8e7MyO53yOO7z3zLeacw4REYmOgrALEBGRYCnYRUQiRsEuIhIxCnYRkYhRsIuIRIyCXUQkYhTsIiIRo2AXEYkYBbuISMQUhXHSHXfc0Q0cODCMU4uI5K1p06Ytd86Vbu11oQT7wIEDmTp1ahinFhHJW2a2sDWvU1OMiEjEKNhFRCJGwS4iEjEKdhGRiFGwi4hEjIJdRCRiFOwiIhETv2BvXAuv/wGWzAy7EhGJk8a18NzlkKpq81PFM9in3ALP/xK036uIZMu7N8KUv8PK+W1+qvgFe6ceMOZKWPAWzH067GpEJA5qFsHb18GeJ8Ggw9r8dPELdoD9LoDSPeGFK6FxXdjViEjUvfxraGmGo3+TldPFM9gLi2Dc7yC5EN67OexqRCTKKj+Aiofg4B9B94FZOWU8gx1g8BjY/Th4689QuyTsakQkilpa4PnLoUtvOPQ/s3ba+AY7wNH/C0318MrVYVciIlE042GomgZjfw3tu2TttPEO9p6D4cCLYfp9/n++iEhQ6uvgpaug334w4sysnjrewQ7wjZ9D51J4/goNfxSR4Lz9F6hbAuN+DwXZjVoFe4cSOPK/oXIKzJwcdjUiEgWrFsC7f4PhZ0D/0Vk/vYIdYO+zoc9IeOm/oWF12NWISL578b+goNC3rYdAwQ7+BzDuD1BTBW9dF3Y1IpLP5r8Oc56EQ38K3fqFUkIgwW5mPzWzWWY208weMLMOQRw3q3Y+yH9sevevsGJe2NWISD5qaoBnf+7Hqx/849DKyDjYzawf8GNglHNuGFAITMj0uKE4+jdQ2N7/YNSRKiLb6r2bYfkncOwfoTi8+9ugmmKKgI5mVgR0AhYFdNzs6tobxvwK5r2idWREZNukquCNP/qJj7sdE2opGQe7c64K+BPwBbAYSDnnXtz4dWY20cymmtnU6urqTE/bdkZPhJ328sMfG9aEXY2I5IsXrwTX7JcrCVkQTTHdgfHAIKAv0NnMztn4dc65Sc65Uc65UaWlpZmetu0UFsHxf4JUpV9uQERka+a9BrMeg8Muy9p6MFsSRFPMWOBz51y1c64ReBQ4OIDjhmfng2HEBN+RuvyzsKsRkVz27w7TQaF2mG4oiGD/AjjQzDqZmQFHAnMCOG64jroaijrAc+pIFZEteO8mWPFp6B2mGwqijX0K8AjwITAjfcxJmR43dF17pTtSX4U5T4VdjYjkolQC3rgWdj8edjs67Gr+LZBRMc65q5xzezjnhjnnznXO1Qdx3NDt//0NOlI1I1VENvJC7nSYbkgzT7eksAiO/zPUJOD13PrBiUjIPnkRZj8Oh/0Muu8cdjVfoWDfmp0Pgn3Pg3/dDIsrwq5GRHJBw2p45jLYcXc45NKwq/kaBXtrHHW13wT7qR/7fQtFJN5e/x2kvoATb4CidmFX8zUK9tbo2N2vqbzoI3j/1rCrEZEwLa7wn+D3/Y7/RJ+DFOytNexUGHwkvPob3xMuIvHT0uw/uXfqCUf9T9jVbJaCvbXM4ITr/A/2ucvDrkZEwvD+rf6T+7jf+U/yOUrBvi26D4TDf+kXCJujRcJEYiWV8J/Yh4z1n+BzmIJ9Wx10CfQaRtPTP2Pq3AU0NreEXZGIZMOzv/Cf2I//s/8En8MU7NuqsBhO/CuFq5cw895fsK5Ro2REIm/OU/DxMzDmipxY5GtrFOzbo2w/Xi05ifOKXqRr9UdhVyMibWlt0i/y1Ws4HPjDsKtpFQX7dnDOcfWa00kW7QRPXAKN68IuSUTayotXQt0yGP83/4k9DyjYt8OSmnUsrCtg6oir/DZYb/w+7JJEpC189jJ8dK+fXdp3n7CraTUF+3Yor0wB0HPkcbDPufDOX6Hqw5CrEpFArauBJy/1ywZ8M7+GOCvYt0NFIklRgbFX3xI4+n+hS7pJpqkh7NJEJCgvXwU1VTD+ppxZZ721FOzbYUZVit16daVDcSF03AFOuB6WzYa3/hR2aSIShPlvwNQ7/PDm/vuHXc02U7BvI+ccFYkUI/t3+/LJ3cfBiDP9HqlLZoRXnIhkrmE1PPkf0GMXGHNl2NVsFwX7NlqwYg2ptY2MLNvhq98Y93vo2AMe/yE0N4ZTnIhk7pWrIbkQTroR2nUKu5rtomDfRhWJJAAjNg72Tj38jLQlFfDO9SFUJiIZW/gvmPJ/fve0gYeEXc12U7Bvo/LKFB2KC9itV5evf3PoSbDXKfD6H9QkI5Jv6uvg8YuhW38Y++uwq8lIIMFuZjuY2SNmNtfM5phZbi5SHICKRJK9+najqHAz/+uO+7Nf9e3Ri6ApGlu/isTCi/8PVi2AU26B9pu4ccsjQd2x3wA875zbAxgJzAnouDmlqbmFmYtSjCjrtvkXde4J42+EZbPg1f/NXnEisv0+eRGm3elHwQw8NOxqMpZxsJtZN+AbwO0AzrkG51wy0+Pmok+X1bGuseXrHacb2+0Y2O98ePdvsOCdrNQmIttp9Qp48kew01A44r/CriYQQdyxDwKqgTvN7CMzu83MOgdw3JzzZcfpFu7Y1zv6Gr8K3OM/8DPYRCT3OAfP/BTWrIRT/i/vJiJtThDBXgTsC9zinNsHWA38cuMXmdlEM5tqZlOrq6sDOG32Ta9M0bVDEQN7tuL3Vvsu/o2SSsALV7R9cSKy7SoehtlP+OV4+4wIu5rABBHsCSDhnJuSfvwIPui/wjk3yTk3yjk3qrS0NIDTZl9FIsmIsm4UFLRykf0BB8AhP/GLCM19pm2LE5Ftk0r45Xj7p/+dRkjGwe6cWwJUmtnu6aeOBGZnetxcs66xmY+X1H59/PrWHH4F9B4OT/4Y6vLzk4pI5LS0+KGNLU1wyt+hoDDsigIV1KiY/wDuM7MKYG/gtwEdN2fMXlxDU4vbesfpxorawSmToL7G727uXNsUKCKtN+Xv8PmbcMw1fumAiAkk2J1z09PNLCOccyc751YFcdxcMiPhl+r9yhoxrdVrqJ/w8PGz8MFtgdYlIttocblfuXG3cX70WgRp5mkrlSeSlHZtT++S7ew1P+BiGHIUvHAlLJkZbHEi0jr1dfDIhdCpJ4y/Oec3pd5eCvZWKq9MMrKsG7a9b4SCAjj5Fr/M7yMXQsOaYAsUka177nJYMQ++NclPJowoBXsr1K5rZP7y1dvecbqxLqV+COTyTzQEUiTbZjwC0++Fwy6DQd8Iu5o2pWBvhRlVKZxr5cSkrRk8xu+fOO0umPV45scTka1b+Tk8/VMoGw2Hf22aTeQo2FuhIt1xmvEd+3pH/D/ot58fJZP8IphjisimNTfC5O8BBqfeBoXFYVfU5hTsrVCRSNK/R0d6dG4XzAELi+HU2/1Y2snfh+amYI4rIl/32m+haiqcdAN03znsarJCwd4K5ZWp4O7W1+sxCE68Hirfg9d/F+yxRcSb9yq8/RfY9zy/V0JMKNi3YkVdPVXJtYwMon19Y8NPg33O9Ztgf/pS8McXibNUlW+CKd3Db10ZIwr2rQi8fX1jx10LvYbDo99Xe7tIUJoa4J/n+81uzrwH2kVywdnNUrBvRXkiSYHB8H5tcMcOUNwRzrgbWprh4fO065JIEF6+ChLvw0l/gx13DbuarFOwb0V5ZZIhO3Whc/uitjtJz8F+8tKij+B5jW8Xycisx+C9m+GAH8Cwb4VdTSgU7FvgnGNGVRt0nG7KnifAwf8BU2/3a0SLyLZb/ik88SMo2x+O+k3Y1YRGwb4Fi1LrWF7X0DYdp5ty5FUw4GB46lJYFsltY0XaTsNqeOhcKGoPp9/lV1aNKQX7FlRU+q3whmfjjh38+PbT7oB2XfwbtL42O+cVyXfOwdP/CdVz/SSkbmVhVxQqBfsWTE8kKS409uzTNXsnLenjw33lPHjsB34Sk4hs2fu3QsWDfmObwUeEXU3oFOxbUFGZYo/eJbQvyvLuKoMO8+2Dc5+GN6/N7rlF8s3nb8Lzv4TdjoVv/DzsanKCgn0zWlocM6tSwSz8tT0OugRGTIDXfwtzng6nBpFct2oBPPwd6DnEL8VboEgDBftmzV++mtr6pm3fCi8oZn7Jgb77wmMXwdLIbSMrkpn6OnjwbHDNcNYD0KEk7IpyhoJ9MyoSvuN0xPZshReU4o4w4T4/a+7Bs2DNyvBqEckl6zejXjYbTrvTzwWRfwss2M2s0Mw+MrNItBtUJFJ0LC5kSGmXcAsp6Qtn3gs1i/wUaa0EKeLXV5rzpO+LGnJk2NXknCDv2C8FIjP4ujyRZHi/bhQV5sCHmv6j4YS/wOdvwEv/FXY1IuGa+wy8do3vgzrokrCryUmBpJaZlQHHA7cFcbywNTa3MHtRTXgdp5uyzzl+ivR7N8O0u8OuRiQcS2bAoxN939OJ10d2M+pMBXU7ej3wCyASg64/XlJLfVMLI/qH1HG6OUdfA4OP9Ft8ffZK2NWIZFeqCu47A9qX+L6n4o5hV5SzMg52MzsBWOacm7aV1000s6lmNrW6ujrT07ap9Uv1Zm0pgdYqLPJTpUv38EO8ls4KuyKR7KivhfvPhPoaOPth3/ckmxXEHfshwElmtgB4EDjCzO7d+EXOuUnOuVHOuVGlpaUBnLbtVCSS7NCpmAE9OoVdytd1KPFv7PZd/N1LzeKwKxJpW81N8M8L/AiY0++G3sPDrijnZRzszrkrnHNlzrmBwATgVefcORlXFqLyRIrh/bphudp+160Mvv0QrF0FD5zpx/OKRJFz8NzP4bOX4Pg/w65jw64oL+TAkI/csrahmU+W1oY3Mam1+oyE0+/0nUmTv+c36hCJmnf/BlPvgEMuhVEXhF1N3gg02J1zrzvnTgjymNk2e3GK5haXWyNiNme3Y+DYP8Inz/kNOpwLuyKR4Mx+wg/vHXoyHPnrsKvJK224LVB+Kq9Md5zm2oiYzRn9fb9exr9u9CtDHvrTsCsSydyCt2Hy9/2GGaf8XWvAbCMF+0YqEkl6lbSnV0mHsEtpvaN+A7VL4OVfQ8fusN/5YVcksv0Wl8P9E6D7QDjrIQ1r3A4K9o1UJLK0FV6QCgr8nqnrkn6Me8ceMPSksKsS2XYr5sE934KOO8C5j0HnnmFXlJf0+WYDqbWNzF++mr3zpRlmQ0Xt4Ix/QL9RMPm7MP+NsCsS2TY1i+AfJwPOh3q3fmFXlLcU7BuYkZ6YlBcdp5vSrrMfBtlzCDz4baj6MOyKRFpnzUp/p752JZwzGXbcNeyK8pqCfQPl65fq7ZeHd+zrdeoB5zzq/77vNKj+JOyKRLasYTXcf4bfDvKsB6DvPmFXlPcU7BuoSCQZ2LMT3ToVh11KZkr6wLmPgxXAPSf7UTMiuahxnd8so2qa3+t30DfCrigSFOwbyMuO083pOdi3UzashrtOhFULw65I5Ksa18FDZ8P81+GkG2HPE8OuKDIU7GnLatexOLUuf9vXN6X3cDjvCahPwd0nQLIy7IpEvKZ6ePg8+OxlOOmvsM/ZYVcUKQr2tIp8m5jUWn339s0ya9PhnkqEXZHEXVODX5300xfghOth3/PCrihyFOxpFYkkBQZ79Y3ghrj99vXNMmtWwt0n+mFlImFoboRHLvDLYBz/Z63/0kYU7GnliRS79epKp3YRnbNVtp8fLVNX7cO9dknYFUncNDfCIxfC3Kfh2Gth/++FXVFkKdgB5xwViWS02tc3pf/+foxw7RK463g1y0j2NNX7O/U5T8Ixv4MDJoZdUaQp2IHEqrWsWtMYnRExWzLgAB/udcvgjnF+CrdIW2pYDQ9MgDlPwbjfw0E/DLuiyFOw8+XEpJxfgz0oAw6E7zwFjWt8uC+ZGXZFElVrk35G6fzXYfxNcODFYVcUCwp2/Pj1doUF7N67a9ilZE/fveGC56CgCO46Dio/CLsiiZq6aj8Sq2oanHYn7JPXG6vlFQU7ML0yydC+JbQritn/jtLd4cLn/WqQ/xivhcMkOKkquPNYWP4ZnPUg7HVy2BXFSsyS7OuaWxwzq1KMjHrH6eZ039mHe/ed4b7TYc7TYVck+W7FPN/EV7fUD7PVPqVZF/tgn1ddx5qG5nh0nG5O195w/jPQexg8fC5MmRR2RZKvvngPbhsLjat9P87OB4VdUSxlHOxm1t/MXjOz2WY2y8wuDaKwbCmvTHec9o/pHft6nXr4f4i7jfO7wr9wJbS0hF2V5JOZk+Huk/wuXt972ffjSCiCuGNvAi5zzg0FDgQuMbOhARw3KyoSKbq0L2KXHbuEXUr42nWGM++F0Rf5PVT/eR40rAm7Ksl1zsHbf/GTj/rt60O9xy5hVxVrGU+zdM4tBhanv641szlAP2B2psfOhopEkr36llBQYGGXkhsKCuG4P/r9Jl/4FdSc6Du/upSGXZnkouYmePZnMO1OGHYqjL8ZivNov+CICrSN3cwGAvsAU4I8bltpaGphzuLa/NwKr60d9EM48x5YOgtuH6sNO+Tr1tX4iUfT7oRD/xO+dZtCPUcEFuxm1gWYDPzEOVezie9PNLOpZja1uro6qNNmZO6SGhqaW+Ldcbole57oO1UbVsNtR8LcZ8OuSHLF8k/9e2Leq36FxrFX+U3VJScE8pMws2J8qN/nnHt0U69xzk1yzo1yzo0qLc2Nj/Xl+b7HaTaU7QcTX/cbdzx4Frz2W3Wqxt3cZ2DSGL9a6HlPaIXGHBTEqBgDbgfmOOeuy7yk7KmoTNKjczvKuncMu5Tc1q0MLnge9j4b3viDD/i1ybCrkmxraYFXr/Ebpfcc7H/hDzos7KpkE4K4Yz8EOBc4wsymp/8cF8Bx25zfCq8b/neTbFFxB7/Wx3F/8rve3HoELJsbdlWSLWuTvj39zT/6X/AXPg879A+7KtmMIEbFvA3kXTKuaWji02W1HDOsd9il5A8zGP196DXMb2t26xF+W7Php4VdmbSlxeXwzwsgudD/Yt//e/69IDkrtr0dM6tqaHHEdymBTOx8EFz0hp+pOvm78NjFUF8bdlUStJYW+NdN6Zmka+A7T/tf7Ar1nBfbYK9IL9WrETHbqaQvnP8sfPNyqHgQ/u8bUPVh2FVJUOqWwf2n+7kMQ8bCD97R8gB5JLbBPr0ySb8dOlLatX3YpeSvwiIY8yt/J9dUD7cfDe/coFEz+e6zl+GWQ+Dzt3zTy4T7oXPPsKuSbRDbYF/fcSoBGHgI/OBt2H0cvPTfcO+3/LKtkl8a1/o1gu49FTr1hImvqeklT8Uy2FetbuCLlWvUDBOkTj3gjHv8ZJUv3oObD4Spd/p1RCT3LXzX36X/60YY9V0f6r32Crsq2U6xDPaKKj8xSR2nATPzk1V++C+/st/TP4G7T4SV88OuTDanvhaeucxvitHS5CccnXAdFGtuRz6LZ7Cnl+odpmBvGz0GwXlPwok3+KFyNx8M794ILc1hVyYb+vRluOlA+OB2OOBi/wt5l8PDrkoCEMtgL0+k2KW0MyUdisMuJbrMYL/z4YfvwS7fhBevhNuP0siZXFC7BB6dCPedCu06wYUvwLG/98s2SyTEMtgrEklGqn09O7r188v+nno7JL+AW8fA45dA7dKwK4ufxnXw1nXwt/1g1mNw2M/gordgwAFhVyYBy3jmab5ZWrOOZbX1DO+nZpisMfOzU3c9Gt68Ft67BWY/Ad/4GRx4MRRpyGmbcs4v3PXilbBqAex+PBz9G7/ei0RS7O7YtRVeiDqU+EC5ZAoMPBRevgpuOsBvoK3RM21j6Sz4x3h46Gwo6uA3lz7rfoV6xMUu2CsSKQoLjKF9FOyh6TkYvv0gnDMZCtv50Ln1CPj0JQV8UJbNhX+eD7ccDIunw7F/9LNHBx8RdmWSBbFriilPJNm9V1c6tisMuxQZMhYu/iaUPwBvXAv3nQZl+/vZrLuM0cSY7VH9iV9aeeZk3xl62GVw0I/8PAOJjVgFu3OOikSK44ZrRcecUVgM+54HIybA9PvgzT/BPadA/wPh8MsV8K1V/bHvGJ3xsG9yOeRSOPjHWgogpmIV7AtXrCG1tlEzTnNRUTs/uWnvb8OH//Ahdc8pULonHHARjDjTD82TL7W0wLxXfGf0vFegqCMcdAkcfKk2H4+5WAV7+b9XdFT7es4qau/XJ9nnXN+cMOUWP4P1lf/x4+L3/57f0SnO6ut889WUv8OKz6BLLxhzJex3gQJdgJgFe0UiRfuiAnbr1TXsUmRrijvAPmf7O/iF7/qAf+cGeOevsPux/g5+t2PiM1TSOah83y+RPGMy1Keg337wrdtg6Hj/iUckLWbBnmSvviUUF8ZuMFD+MvOrRw48BFYthA9ug/IHYe7T0KEb7HWKb5/vfwAURPDnumIeVDzk/6xa4Jtb9jwBRl8E/fcPuzrJUbEJ9qbmFmZW1XDm/tqnMW9139mPgz/yKvj8dSh/CCoehml3wQ4DYM+T/CSoAQfl7x2sc7B0Jnz6op9UVDUNML8swzcvhz1PhPb6xClbFptg/6y6jrWNzZqYFAWFRX6o5JCxvr157tM+4N+f5JedbdcVBh/uQ37IUVDSJ+yKt6y+Fua/4cP805egdpF/vs9IOOpqGH6637FKpJUCCXYzGwfcABQCtznnfh/EcYNUUemX6tWImIhp3wVGTvB/6uvg8zfh0xd8QM55yr+mxy7Qb5QfI1+2H/QaHt4dfUsLrJwHiamQ+ACqpvrZoS1N0L4EBo9J/0IaC101LFe2T8bBbmaFwE3AUUAC+MDMnnTOzc702EGankjStX0Rg3pqBbvIat8F9jjO/3EOls3227xVvu8Df8bD/nWF7aHPCNhxNx/6PQdDzyH+66BWOGxu9H0CK+f5dvKV8/wIlkXTYZ0fnUW7rtBvXz/mfJcxMOBAP65fJENB3LGPBj5zzs0HMLMHgfFATgX7jESK4WXdKCjQZJdYMPM7AK3fBcg5qKn68k550XT47BWou++r/13nnfy2cB27+9maHXeAjj3S7dqbeO80rYU1K2HtKlib/nvNKqhdDG6D9efbd4Oeu/gRLGX7Q9ko/4ulQDOgJXhBBHs/oHKDxwnga+uAmtlEYCLAgAEDAjht69U3NTN3SQ3fPXSXrJ5XcoiZH//erQz2OvnL5+vr/A5P6++skwu/DOeVn/uwXrMSmus3c9zCDX4JdIeSMt/UU9LXfxLoMdj/3amnZtBK1mSt89Q5NwmYBDBq1KisrvQ0Z3Etjc1OW+HJ17Xv4ptl+ozY8uuamzb9fEGhAltyThDBXgVsOIawLP1czqhYP+O0vzpOZTsVxmYAmURAEDM6PgB2NbNBZtYOmAA8GcBxAzO9MsmOXdrTt1uHsEsREWlzGd+GOOeazOxHwAv44Y53OOdmZVxZgCoSKUaWdcP0kVlEYiCQz5fOuWeBZ4M4VtDq6puYV13HiSM0wUNE4iGCi2t81YxECudghGacikhMRD7Y13ecjtSMUxGJiRgEe4qy7h3p0TlPF4USEdlGkQ/28kRSd+siEiuRDvYVdfUkVq3VjkkiEiuRDvaKKq3oKCLxE+1gr0xhBsN1xy4iMRLtYE8kGVzahS7tNR1cROIjssHunKM8kVL7uojETmSDfXFqHcvr6jUiRkRiJ7LB/u8VHXXHLiIxE9lgn16ZorjQ2LNPSdiliIhkVWSDvSKRZI/eJXQo1tZjIhIvkQz2lhbHDHWcikhMRTLYP1+xmtr6JnWcikgsRTLYv9wKT3fsIhI/kQz28soUHYsLGVLaJexSRESyLpLBXpFIMqxfCUWFkbw8EZEtilzyNTa3MGtRjRb+EpHYyijYzexaM5trZhVm9piZhZ6mnyytpb6pRSNiRCS2Mr1jfwkY5pwbAXwCXJF5SZmpSPilejUiRkTiKqNgd8696JxrSj98DyjLvKTMVCSSdOtYzM49O4VdiohIKIJsY78QeC7A422X8ko/McnMwi5FRCQUWw12M3vZzGZu4s/4DV5zJdAE3LeF40w0s6lmNrW6ujqY6jeyrrGZT5bWqn1dRGJtqztQOOfGbun7ZnY+cAJwpHPObeE4k4BJAKNGjdrs6zIxa1ENTS1OI2JEJNYy2lrIzMYBvwC+6ZxbE0xJ26+80s843bu/gl1E4ivTNvYbga7AS2Y23cz+HkBN260ikaRXSXt6lXQIswwRkVBldMfunBsSVCFBqEik1AwjIrEXmZmnqbWNzF++mpHqOBWRmItMsM+s8hOTdMcuInEXmWAvTy/VO7yf7thFJN4iE+wVlSl27tmJ7p3bhV2KiEioohPsiaSaYUREiEiwV9fWsyi1Th2nIiJEJNj/vRWe7thFRKIR7OWJFAUGw/qVhF2KiEjoIhHsFYkku+7UlU7tMppvJSISCXkf7M659IxTta+LiEAEgj2xai0rVzcwQgt/iYgAEQj29ROT9lbHqYgIEIFgr0ikaFdYwO69u4ZdiohITsj7YC+vTLJn3xLaFeX9pYiIBCKv07C5xTGzKqWJSSIiG8jrYJ9fXcfqhmZNTBIR2UBeB3t5wi/Vqzt2EZEv5XWwVySSdG5XyC6lXcIuRUQkZ+R1sJcnUgzr143CAgu7FBGRnBFIsJvZZWbmzGzHII7XGg1NLcxZXMNITUwSEfmKjIPdzPoDRwNfZF5O6328pJaGphYtJSAispEg7tj/AvwCcAEcq9XWzzgdqRExIiJfkVGwm9l4oMo5Vx5QPa1WkUjSvVMxZd07ZvvUIiI5bavr3JrZy0DvTXzrSuBX+GaYrTKzicBEgAEDBmxDiZvmV3TcATN1nIqIbGirwe6cG7up581sODAIKE+HaxnwoZmNds4t2cRxJgGTAEaNGpVRs82ahiY+WVrL0UN7ZXIYEZFI2u6dKZxzM4Cd1j82swXAKOfc8gDq2qJZi2pocdoKT0RkU/JyHHt5ZXqP0/4aESMisrHA9pIGizf1AAAEp0lEQVRzzg0M6lhbU55I0bdbB3bq2iFbpxQRyRt5ecdekUiqGUZEZDPyLtiTaxpYuGINwzUxSURkk/Iu2GdUrV/RUXfsIiKbknfBXpFeqld37CIim5Z3wV5emWTQjp3p1rE47FJERHJS3gW7n3Gqu3URkc3Jq2BfWrOOJTXrNCJGRGQL8irY109M2lsTk0RENiuvgr0ikaKwwBjaR8EuIrI5eRXsZd07cuq+/ejYrjDsUkREclZgSwpkw4TRA5gwOvMlf0VEoiyv7thFRGTrFOwiIhGjYBcRiRgFu4hIxCjYRUQiRsEuIhIxCnYRkYhRsIuIRIw557J/UrNqYGHWT/xVOwLLQ64hG+JynRCfa43LdUJ8rrW117mzc650ay8KJdhzgZlNdc6NCruOthaX64T4XGtcrhPic61BX6eaYkREIkbBLiISMXEO9klhF5AlcblOiM+1xuU6IT7XGuh1xraNXUQkquJ8xy4iEkmxC3YzG2dmH5vZZ2b2y7DrCZKZ3WFmy8xs5gbP9TCzl8zs0/Tf3cOsMQhm1t/MXjOz2WY2y8wuTT8fxWvtYGbvm1l5+lr/J/38IDObkn4fP2Rm7cKuNQhmVmhmH5nZ0+nHUb3OBWY2w8ymm9nU9HOBvX9jFexmVgjcBBwLDAXOMrOh4VYVqLuAcRs990vgFefcrsAr6cf5rgm4zDk3FDgQuCT9c4zitdYDRzjnRgJ7A+PM7EDgD8BfnHNDgFXAd0OsMUiXAnM2eBzV6wQY45zbe4NhjoG9f2MV7MBo4DPn3HznXAPwIDA+5JoC45x7E1i50dPjgbvTX98NnJzVotqAc26xc+7D9Ne1+CDoRzSv1Tnn6tIPi9N/HHAE8Ej6+Uhcq5mVAccDt6UfGxG8zi0I7P0bt2DvB1Ru8DiRfi7KejnnFqe/XgL0CrOYoJnZQGAfYAoRvdZ088R0YBnwEjAPSDrnmtIvicr7+HrgF0BL+nFPonmd4H85v2hm08xsYvq5wN6/ebXnqWTGOefMLDLDoMysCzAZ+Ilzrsbf4HlRulbnXDOwt5ntADwG7BFySYEzsxOAZc65aWZ2eNj1ZMGhzrkqM9sJeMnM5m74zUzfv3G7Y68C+m/wuCz9XJQtNbM+AOm/l4VcTyDMrBgf6vc55x5NPx3Ja13POZcEXgMOAnYws/U3ZlF4Hx8CnGRmC/BNpEcANxC96wTAOVeV/nsZ/pf1aAJ8/8Yt2D8Adk33tLcDJgBPhlxTW3sS+E766+8AT4RYSyDSba+3A3Occ9dt8K0oXmtp+k4dM+sIHIXvU3gNOC39sry/VufcFc65MufcQPy/y1edc2cTsesEMLPOZtZ1/dfA0cBMAnz/xm6Ckpkdh2/LKwTucM5dE3JJgTGzB4DD8SvFLQWuAh4HHgYG4FfUPMM5t3EHa14xs0OBt4AZfNke+yt8O3vUrnUEviOtEH8j9rBz7moz2wV/Z9sD+Ag4xzlXH16lwUk3xfzMOXdCFK8zfU2PpR8WAfc7564xs54E9P6NXbCLiERd3JpiREQiT8EuIhIxCnYRkYhRsIuIRIyCXUQkYhTsIiIRo2AXEYkYBbuISMT8fxLID9mpDE+ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167dc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1,y2)#错误"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116851390>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168510f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1)\n",
    "plt.plot(x,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1168c1d30>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168c1978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y1)\n",
    "plt.plot(x,y2,color='r', linewidth=2.0, linestyle='-.')#'-','-.',':'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116d61b00>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c035c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y2,'-.b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 折线图\n",
    "\n",
    "Label标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'自变量')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a55be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4],[2,3,3,3])\n",
    "plt.ylabel(\"Some Num\")\n",
    "plt.xlabel('自变量')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 支持中文\n",
    "\n",
    "设置字体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "plt.rcParams['font.family']='SimHei'\n",
    "plt.rcParams['font.size']=14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'自变量')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170685c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4],[2,3,3,3])\n",
    "plt.ylabel(\"Some Num\")\n",
    "plt.xlabel('自变量')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 散点图\n",
    "\n",
    "多一个标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117384c50>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117210e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4],[2,3,3,3],'bs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linestyle:\n",
    "\n",
    "ro---红色的圆点\n",
    "\n",
    "bs---蓝色的方块\n",
    "\n",
    "g^---绿色的三角"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11745d080>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117372da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4],[2,3,3,3],'g^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=np.linspace(-5,5,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117537400>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD+CAYAAAAkukJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAH49JREFUeJzt3Xt8VPWd//HXZ3LhLgFCBbEhAooggkq0paCodH/1Vq1VW3uv6NK7u9vaumt3V221u21tu921dUt/VG2l2i60Vbz9XKssgngJEAQXVCJXE2uCgYBAIJnP748zQyYxDDC3M5l5Px+PeZD5fs/M+ZyEmfe5fo+5OyIiIocSCbsAERHJbwoKERFJSkEhIiJJKShERCQpBYWIiCSloBARkaQUFCIikpSCQkREklJQiIhIUqVhF5AJlZWVXl1dHXYZIiK9yooVK5rdffjhpiuIoKiurqa2tjbsMkREehUz23wk02nXk4iIJKWgEBGRpBQUIiKSlIJCRESSUlCIiEhSCgoREUlKQSEiIkkpKEREeqvn7oJ1i7I+m7SDwszGm9kyM9ttZkvNbHysvcrMnjazvWa22symJrwmpT4REYlpb4OnvwevPp71WWVii+J+4DHgJGAtMDfWfjewCzgRWAwsMLNImn0iIgKwcQm0tcKEy7I+q7S+gM2sAtgD/MjdG4AHgYlmNho4H7jF3bcBtwBVwDmp9qVTp4hIwVn3EJQPgjEzsz6rtMZ6cvcdwAwAMysHPg6sirW1AXWx6VrMrB6YDoxKsW9xOrWKiBSMaAesfwRO+hCU9sn67DI5KGBr7DEDuBRocfdoQv92YCRBEKTS14WZzQHmAFRVVWVwMURE8tyW5bBnO0z4cE5ml8l9/9OAFcB8wAHrYV6eRl8X7j7X3WvcvWb48MOOkisiUjjWLYLSvjDugzmZXbrHKIab2RkA7r4KuAmoAQYCQ7odhB4KNMYeqfSJiEg0GgTF2FnQZ2BOZpnuFsXpBGc8xcXX/BcDZcAZAGY2FBgLPBN7pNInIiINq6D1jZztdoL0g+IFoMTMvmJmxwNfBzYAzwJPATfH2m8BNgLL3H1rKn1p1ikiUhjWPQSRUhh/Qc5mmVZQxM56uhy4FlgPnABc7u4HgGsIdkFtAGYCVyUcpE61T0SkeLkHQXHCOdBvSM5mm/ZZT+7+DLFdRd3atwLnHeI1KfWJiBS1xjp4+3WY/jc5na2ueBYR6S3WLIBIGUy4NKezVVCIiPQG0Si8/EcYNwv6D83prBUUIiK9wZblwdlOk67M+awVFCIivcHaBVDWH8ZfmPNZKyhERPJdxwF4+U9BSOToIrtECgoRkXz3+mLY+3You51AQSEikv/WLIC+g4MD2SFQUIiI5LP9e2D9w8EpsTkYUrwnCgoRkXy27iHYvxumXB1aCQoKEZF8tuo+GFINo6eHVoKCQkQkX7Vsgk3PwGmfAut+q57cUVCIiOSruvsBgymfCLUMBYWISD6KRqHutzBmJlS8N9RSFBQiIvlo0xLYuQVO+3TYlSgoRETy0qr50GcwTLgk7EoUFCIieWfvjuC+2JM+CmX9wq5GQSEikndWPwDte2Hq58KuBFBQiIjkF3eonQejpsJxp4ddDaCgEBHJL5uegeZX4czrwq7kIAWFiEg+efH/Qr8hcMrlYVdyUNpBYWbVZvaUme0ysxfM7NRY+2QzW2Fme81sqZmdkPCalPpERApaayOsexhO/3ReHMSOy8QWxVygAZgEbAXmm1kEWAjUAuOARuAegFT7REQK3spfg3fA1GvCrqSL0nRebGblwCzgNHffbGbzgEeAs4GxwDR3bzaz24A6M6sGRqfS5+6b0qlVRCSvdbTDintg7CwYNjbsarpId4uiDLgReD32fBjgwAyg3t2bY+1rgDZgehp9IiKFa92DsKshrw5ix6W1ReHu7wB3AJhZGXA98HtgJNCcMF3UzFpi7an2iYgUJnd49k4YOhZOuiDsat4lI2c9mVkpMB8YCHyJYKui+5i4kVh7qn3d5znHzGrNrLapqSntZRARCc3mZ6FhJUz7CkTy72TUTJz1FAEeACYC57l7C8FB6Mpu01TE2lPt68Ld57p7jbvXDB8+PN3FEBEJz/I7of+w0IcTP5RMRNd3gQnATHd/M9a2BBhjZvFv8ClAObA0jT4RkcLT/Bq88mhwbKK8f9jV9CitoDCz0cA3gC8CHWZWYWYVwPPABuB2MxsFfBtY7O5bgGUp9omIFJ7lP4OSPnDmX4ddySGlu0VxLtCHYEugJeExHbgSmArUAyOA2QDu7qn0iYgUnHeaYfX9MOVqGJi/u9DTPevpXuDeJJNMPcTrXkqlT0SkoCy/E9rbYNpXw64kqfw7vC4iUgz2vA0v/DK458Twk8KuJikFhYhIGJb/DPbvhnO+GXYlh6WgEBHJtb0t8PwvYOJl8J4JYVdzWAoKEZFce+4u2L+rV2xNgIJCRCS39u6A5/4TTr4ERpwadjVHREEhIpJLz/4HtO2Emd8Ku5IjpqAQEcmVXW8GB7FP+SiMnBJ2NUdMQSEikiuL/xWiB2DWP4VdyVFRUIiI5ELza8Ed7Gpmw9AxYVdzVBQUIiK58Odbg/tgn9N7jk3EKShERLJt6wuwbhF84Pq8HtPpUBQUIiLZFO2AR78Jg0YGNybqhdIaFFBERA5j5a+hsQ6umAd9BoZdTUq0RSEiki173g6OTYyeAZOuCLualCkoRESy5anbYF8rXPQDMAu7mpQpKEREsqGhDmp/BWfNgWNPCbuatCgoREQyreMAPPQ1GDAczv37sKtJmw5mi4hk2rP/Dm++BB/7DfSrCLuatGmLQkQkk5peCYbqmPgRmHhp2NVkhIJCRCRToh3w4FehfABc9MOwq8kY7XoSEcmU534O216Aj/4SBr4n7GoyJiNbFGY20swWm1lNQttkM1thZnvNbKmZnZBun4hI3mpcDU/eGtyQ6NSrwq4mo9IOCjObBzQAMxPaIsBCoBYYBzQC96TTJyKSt/a/AwuuDc5yuvQ/evU1Ez3JxK6nG4HvAhsT2s4GxgLT3L3ZzG4D6sysGhidSp+7b8pArSIimffYjbB9A3zuIeg/NOxqMi7toHD3ZqDZuiboDKA+1gewBmgDpgPVKfZtSrdWEZGMW/sHWPUbmPF1OOGcsKvJimyd9TQSiH/Z4+5RoCXWnmpfF2Y2x8xqzay2qakpS4shIpLEX/43OMvp+DPhvJvCriZrshUUDnTfSReJtafa13UG7nPdvcbda4YP733ju4tIL7e3BR74ZDAi7Md+AyVlYVeUNdk6PbYRqIw/iR2kroi190mxT0QkP0Q7YOFfw85t8PmH4Zh37fQoKNnaolgCjDGz+Kr+FKAcWJpGn4hIfnjqu7Dhv+HC70PV+8OuJuuyFRTLgA3A7WY2Cvg2sNjdt6TRJyISvhfnwdKfwNRroGZ22NXkRFaCwt0duBKYCtQDI4DZ6fSJiIRu/SPw6A1w0gVw0R0Fd73EoWTsGIW7W7fnLxF84fc0bUp9IiKh2fpicFHdcafDlb+CkuIZAUmDAoqIHE5DHcy/AgaNgE/8Lhj0r4goKEREkml8CX59GfQ5Bj77IAwsvtPxFRQiIofyl5eDkCgfCJ9bBENGh11RKBQUIiI92foi3HMxlPaFzy+CocU7kLWCQkSku1efgHs/DH0r4JpHYOiYsCsKlYJCRCRR3f1w/9VQeSJc+0TRhwToDnciIoFoB/z5Vlj202AU2I/Ph77HhF1VXlBQiIjs3QELrwuG5aiZDRd8H0rLw64qbygoRKS4NdTBgtmwYzNc/GM489qwK8o7CgoRKU7RKDz3c3jyluAWpp9bBKM/EHZVeUlBISLFZ8dWWHQ91D8FJ18S3Oe6AG9hmikKChEpHtGOYPTXP98KHg12NdXMLprB/VKloBCR4vDGSnjsW7DtRRg7Cy75SdFeaX20FBQiUthaG+DP34HV9wfHIi7/BUz+uLYijoKCQkQK0+4mePanwa6maDtM/1s4+xu6NiIFCgoRKSytDbD8Z1D7K2jfB6deBefdBEOqw66s11JQiEhheGMFPHcXvPzH4ED1qVfBOd8MhuKQtCgoRKT32vM2rFkAq34Db74E5YPgrC/A++ZoCyKDFBQi0rvsa4VXH4eX/wQbnoSONhg5JbiH9eSP6xhEFigoRCT/ba8PQuG1J2DjM0E4DBoZXANx2idh5OSwKyxoeRkUZjYZuBuYCKwAPuPuG8OtSkRywh3efh22PAeblsLmpbBjS9A3dGwwFtPEy+D4syCiOyXkQt4FhZlFgIXAU8ClwL8B9wAzQyxLRLKhox22b4C/rA1uO9pYF1wYt29H0N9vKFRPh2lfg3GzYNjYcOstUnkXFMDZwFhgmrs3m9ltQJ2ZVbv7pnBLE5Gj4h586bc2ws6twZZBy6Zgi6H5NWjZGFzjABApheETgq2FUVPh+DNh+MnaasgD+RgUM4B6d2+OPV8DtAHTgU2ZnNFbrftYuPINSiIQMYs9oCRiRCLB8xIzLN5m8fbO6UsSnyf8bAYlCW0We69I9/eL9SW+pxlEIobBoaeJdPbFX2N0e64rTyUToh1wYG9wTcL+dzofbTuhbVdwcHnfDtjbEpyFtGc7vNMEu9+CXW9C+96u71fSJ7j/9PDxMOESqBwPIyYF/2bxHhDuTtQh6k7UHfcgx+LPo97zNPG+aNSDX0fCNInTd0Q7p++Idn2/oC9h2vhroxz8uSOaWEtnXzT2Xp3Tdc6rw52xwwfyVxOPzdrvDfIzKEYC8ZDA3aNm1hJrz6iGnfv4/uPrM/22eSMIj85wwQhCh87wsdh0icEU5Et8mlgbsel7aovPK/Fn7N1tie2xAs2dfuxlgO+hP/vo6/voyz76ehvl3kY5+yn3/ZRxgDJvp8wPUEo7JbRTSgcl3kEJHUSIUkKUCFHMo0RwInRgeLcHmDtmTqwKiLVD8EVwuHjNRfx6rBYA8649Pf1sOOCxaT3heXy5oxhOxKNYwu8p+P21E/EOSj32e/V2SjlAmR84+Ps+EgcoZbcNZGdkMDusgh1WxfaSKWwvHUZzZBh/sUoa7VhabDDRfYa/AWyLfeHSjHszTvAFCV2/tD323D1Y6sQvcbpM09nnJH6hH/3foLe4ZPLIogyKxE9wXISunxDMbA4wB6CqqiqlGU0eNZh137ngYMIfTO5oZ6p3RIP/uI4ntB/dGkTHwbWTzrWG+M8H/zNH/eCHpCPhP3/Uu03jXf/zd66ddM47cS0p/oGJz6vL+8b6El+b+IFz7/qBO9hGwrzo2uYOFm1nSPtfqDjQTEVHM4PbmxnU3sIxHS0M6tjBwGgrA6K7GBDdRf/oO0SIpvT3OxBEBR0Wj4ggLtwOfhXiB/+NR0FibHT5HxXrPzKek7g4xPwSthS7L4N3W1bMEn4Pwe8iaiVEKSNKhA4roYMSPBKLXiulw0o4QDkHImUcoIwD1of9kXIOWB/arB9tkX60WV/2lQxgr/VnX8lA3okMYn+kb2xlonPlgIQVg4gZwwwqYysMJLTHpzm4MkLnVvnBlYzEFZv4yk+310cih966jq8klUQ6t7bjewSMhK3/WHu8lvieABL6Dm75W9efS2IrXPG9Ep3v1dl/uD0MkUjnnozO1xiRSGctkUjne5XmYNdcPgZFI1AZfxI7uF0Raz/I3ecCcwFqampSWl+IRIx+5SWpV1rM3IP9zW/9b7CveftrwSmMLZthV0NwZWyikj4w8D1wzDDofzz0GwL9KqBvRXDee59B0OcYKOsP5QOCf8v6Qmk/KO0TPErKg38jZRApocyMsnCWXqSo5GNQLAFuM7Ph7t4ETAHKgaXhllXE4qcrvrEyGCahYVUQEG2tndP0rwzOSKmeARVVwWPwKBh0HAwaAX0Ha7ROkV4qH4NiGbABuN3MbgW+DSx29y3hllVkttfD60/DpmXBuezvvBW0l/WHEZODK2CPPSV4DBunu4OJFLC8Cwp3dzO7kuCCu3qgFvhMuFUVgY522LwMXnkUXvtveLs+aB90HIyZGdxL+Pgzg9MXS/Luv42IZFFefuLd/SVgath1FDx32LIc1vwXrFsUnNJY2heqz4b3fQHGfRCGjtEuI5Eil5dBIVnW2girfwur7guOPZT1h5M+BBM/Aif+HyjvH3aFIpJHFBTF5I2VsfH6/xBcDVt9NpzzLZh4aXCmkYhIDxQUhc4dNi6B//lBMLha+SA4aw6ceZ3GzRGRI6KgKGQbn4Gnvwdbng2GZP7Q9+D0z2i8fhE5KgqKQrS9Hp74x+AMpkEj4cIfwhmfDS5gExE5SgqKQtK2Gxb/Czz/i+AK5ln/DO//MpT1C7syEenFFBSFYsOTsOjvYOeWYPfS+f8Eg7I7UJiIFAcFRW+3byc8diOsvh8qT4JrHofR08KuSkQKiIKiN9v6Iiy8FnZug3O+CWffoOMQIpJxCoreKBqFpT8OzmgaPApmPw7vPSvsqkSkQCkoept9rfDHLwRnNE26Ai75STAyq4hIligoepPt9fDAJ4P7P1z4g+DCOY3DJCJZpqDoLTY/C/dfDRaBz/wxGNFVRCQHFBS9wfpHYcE1MPh4+NSC4Mb0IiI5kv2brUp6Vv4Gfvep4AZBs59QSIhIziko8tnzc+Ghr8KY8+CzD8GAYWFXJCJFSLue8tULv4THvgknXwJX3g2l5WFXJCJFSlsU+aj2bnj0Bhh/kUJCREKnoMg3q38HD/8tnPghuOoehYSIhE5BkU/qn4YHvxzcee5jvw5GgBURCZmCIl80vgS/+wxUjoer52vMJhHJG2kHhZmNNLPFZlbTrX2yma0ws71mttTMTki3r2Dt3AbzrwruPPep/9KQHCKSV9IKCjObBzQAM7u1R4CFQC0wDmgE7kmnr2Ad2BsMy3FgT3Ax3eBRYVckItJFuqfH3gh8F9jYrf1sYCwwzd2bzew2oM7MqoHRqfS5+6Y0a80/7vDw30HjavjE7+DYiWFXJCLyLmkFhbs3A8327oHpZgD1sX6ANUAbMB2oTrFvUzq15qXnfxHccOjcm2D8BWFXIyLSo8PuejKze81sRw+Pf0nyspFA/Msed48CLbH2VPsKy+Zn4f/dBOMvDm46JCKSp45ki+IG4OYe2luTvMaB7psZkVh7qn1dmNkcYA5AVVVVklLy0J63YeF1MKQaLv9PiOjkMxHJX4cNCndvApqO8n0bgcr4k9hB6opYe58U+7rXNReYC1BTU/OuIMlb7vDQ12D3W3Ddk8GZTiIieSxbq7JLgDFmNjz2fApQDixNo68wrLgb1j8MH7wZjjst7GpERA4rW0GxDNgA3G5mo4BvA4vdfUsafb3fW+vh8Ztg7Pnw/q+EXY2IyBHJSlC4uwNXAlOBemAEMDudvl6vox3+9EUo7w8f0XEJEek9MjLMuLu/6/xYd3+J4Au/p+lT6uvVlt8JDauC0WAHHRt2NSIiR0yrtbnQ/Bo8/b3g3hKnXB52NSIiR0VBkW3RaHCWU1k/uPhH8O6LE0VE8prucJdttfNgy3L4yF0waETY1YiIHDVtUWTT7rfgz98J7nk95RNhVyMikhIFRTY9eWswOuxFP9QuJxHptRQU2bL1Rai7D6Z9BSpPDLsaEZGUKSiyIdoBj94Ag0ZqwD8R6fV0MDsbVv4aGuvginnQZ2DY1YiIpEVbFJnWtguevh2qPgCTrgi7GhGRtGmLItOW/wzeaQruWKcD2CJSALRFkUm734Jl/w4TL4PjC28UEhEpTgqKTPqfH0D7Pjj/n8OuREQkYxQUmbK9PrjXxNTPQ+W4sKsREckYBUWmPH07lJTDzBvDrkREJKMUFJnw1npY+wd43xc1hLiIFBwFRSY8cweU9YdpXw27EhGRjFNQpKv5NVi7EM66DgYMC7saEZGMU1Cka8kdUNoXpn0t7EpERLJCQZGO7fWw5vdQMxsGDg+7GhGRrFBQpOOZHwdnOn3g+rArERHJGgVFqlob4KUH4IzP6UwnESloaQWFmVWb2VNmtsvMXjCzUxP6JpvZCjPba2ZLzeyEdPvyyvO/AI/CtC+HXYmISFalu0UxF2gAJgFbgfkAZhYBFgK1wDigEbgnnb680rYLau+GCZfCkOqwqxERyaqUR481s3JgFnCau282s3nAI2ZWAUwBxgLT3L3ZzG4D6sysGhidSp+7b0q11oxbdR+07YQP6EwnESl86QwzXgbcCLweez4McGA/MAOod/fmWN8aoA2YDlSn2LcpjVozp6Mdlv8cqqbB8TVhVyMiknWH3fVkZvea2Y7uD+Af3f0Od3/HzMqA64Hfu/seYCQQ/7LH3aNAS6w91b7udc0xs1ozq21qakpp4VOy7iHYuUVXYYtI0TiSLYobgJt7aG8FMLNSgmMTA4Evxfoc6H7XnkisPdW+Ltx9LsExEmpqat7VnzXL74ShY2D8hTmbpYhImA4bFO7eBPS4yh47+PwAcDJwnru3xLoagcpu01XE2vuk2Be+N1YEjwt/AJGSsKsREcmJdM96+i4wAZjp7m8mtC8BxphZ/HLlKUA5sDSNvvC9OA/KBsCUq8OuREQkZ1IOCjMbDXwD+CLQYWYVsUcpsAzYANxuZqOAbwOL3X1LGn3h2vN2MPjf5I9B38FhVyMikjPpbFGcS7CraAnBAef4Y4a7O3AlMBWoB0YAswFS7Qtd3W+D25yeeW3YlYiI5JQF3829W01NjdfW1mZvBtEo3FkDAyrh2ieyNx8RkRwysxXuftjz/DXW05HYuBjeroczrwu7EhGRnFNQHIkX50H/YTDxsrArERHJOQXF4ex6E155DE7/NJT2CbsaEZGcU1AczuoHwDvg9M+GXYmISCgUFMm4Q918eO/7oHJc2NWIiIRCQZHMtlpofhVO+1TYlYiIhEZBkUzdfVDaD065POxKRERCo6A4lP17YO0fgjOd+h4TdjUiIqFRUBzK+oehrRVO124nESluCopDWXUfVFTB6BlhVyIiEioFRU92boONS4KD2BH9ikSkuOlbsCdr/wA4nHpV2JWIiIROQdGTtQvguDNg2NiwKxERCZ2Corvm16BxNZx6ZdiViIjkBQVFd2sWAAanfDTsSkRE8oKCIpF7sNupegYcMzLsakRE8oKCIlHjati+QbudREQSKCgSrV0AkTKYcGnYlYiI5A0FRVw0GpwWO24W9B8adjUiInlDQRG37UVofQMmXRF2JSIieSWtoDCz8Wa2zMx2m9lSMxuf0FdlZk+b2V4zW21mU9Pty6p1D0FJOZx0QU5mJyLSW6S7RXE/8BhwErAWmJvQdzewCzgRWAwsMLNImn3Z4Q7rFsGYczVSrIhINyl/AZtZBbAH+JG7NwAPAhNjfaOB84Fb3H0bcAtQBZyTal+qdR6RN9fAjs0w4cNZnY2ISG9UmuoL3X0HMAPAzMqBjwOrYt0zgDagLjZti5nVA9OBUSn2LU611sNatwgsAuMvytosRER6q8MGhZndC1zWQ9dd7v4PsZ9bY4/4mNwjgRZ3jyZMvz3W3pZiX/asWwSjp8OAyqzORkSkNzqSLYobgJt7aG9N+Hka8D1gvpmdBThg3aaPxNpT7evCzOYAcwCqqqqOYDEOofk1aFoHNT9M/T1ERArYYY9RuHuTu2/q/gBKzOyM2DSrgJuAGmAS0AgM6XYQemisPdW+7nXNdfcad68ZPnz4USxyN+sWBf+efHHq7yEiUsDSOZvodIIznuLia/1R4BmgDDgDwMyGAmNj7an2Zce6RTCqBgaPytosRER6s3SC4gWCrYqvmNnxwNeBDcCr7r4VeAq4OdZ3C7ARWJZqXxp1HtqOrdCwUmc7iYgkkXJQxM56uhy4FlgPnABc7u4HYpNcAwwkCI+ZwFUJB6lT7cusA3tg/MUKChGRJMz9XceJe52amhqvra0NuwwRkV7FzFa4e83hptNYTyIikpSCQkREklJQiIhIUgoKERFJSkEhIiJJKShERCQpBYWIiCSloBARkaQK4oI7M2sCNoddRwoqgeawi8gxLXNxKLZl7q3LO9rdDzuqakEERW9lZrVHclVkIdEyF4diW+ZCX17tehIRkaQUFCIikpSCIlxzwy4gBFrm4lBsy1zQy6tjFCIikpS2KEREJCkFRR4ws/PNzM3s3LBryTYzqzazp8xsl5m9YGanhl1TtpjZZDNbYWZ7zWypmZ0Qdk3ZVkx/30SF/hlWUITMzMqAn4VdRw7NBRqAScBWYH645WSHmUWAhUAtMA5oBO4Js6YcKYq/b6Ji+AyXhl2A8A1gG/DesAvJNjMrB2YBp7n7ZjObBzxiZhWxW+sWkrOBscA0d282s9uAOjOrdvdN4ZaWHUX2901U8J9hbVGEyMyqgG8BXw27lhwpA24EXo89HwY4sD+0irJnBlDv7vGrddcAbcD08ErKumL6+wLF8xnWFkWWmdm9wGU9dN0FjAfucvdXzCy3hWVRsmV293+ITVMGXA/83t335LK+HBlJwpAO7h41s5ZYe0Fy93eAO6Ao/r5x/0YBfoa7U1Bk3w3AzT20Twc+AXw6t+XkxKGWuRXAzEoJ9l0PBL6Uw7pyyYHu3xyRWHtBK5K/L2Z2IXAGhfkZ7kJBkWXu3gQ0dW83s5sJ1i4bYmsiA4CHzew2d//X3FaZWYdaZjh4kPcB4GTgPHdvyWVtOdRIMFAccHC5K2LtBauI/r4AH6NAP8Pd6YK7kJhZJcEaV9zLwLXA44V84M/Mbgc+Apzj7tvDridbzGwGsAQ41t2bzOx0YCXBaJ1bwq0ue4rl7wvF9RlWUOQJM9sNXOLui8OuJVvMbDTwCvBXBAd343a7e3s4VWWHBauYrwCLgVuBnwLD3P28MOvKpmL6+/akkD/DOutJculcoA/BmnZLwmNGiDVlhQdrYFcCU4F6YAQwO9Sisu9ciuTvW2y0RSEiIklpi0JERJJSUIiISFIKChERSUpBISIiSSkoREQkKQWFiIgkpaAQEZGkFBQiIpLU/weGbvq4ayM7IAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117537748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,t**2)\n",
    "plt.plot(t,t**5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11771e400>,\n",
       " <matplotlib.lines.Line2D at 0x11771e5c0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117661828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#多个函数图，可以合并成为一个函数\n",
    "#但必须要求(自变量，因变量，style字段)\n",
    "plt.plot(t,t**2,'r--',t,t**5,'y-.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结构化数据绘制散点图\n",
    "\n",
    "原始2个类型之间比较，即可是离散型，也可是连续型的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data={\n",
    "    'a':np.arange(50),\n",
    "    'c':np.random.randint(0,50,50),\n",
    "    'd':np.random.rand(50)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
       " 'c': array([19, 47, 35, 40, 27,  0, 26, 40, 45, 46, 45,  1, 32, 40, 41,  9, 30,\n",
       "         8, 20, 18, 37, 33, 45, 12, 20, 18, 34,  8, 31, 10, 43, 21,  8, 17,\n",
       "         6, 30, 12, 22, 33, 33, 20, 12, 49, 20, 27,  7, 34, 48, 43, 26]),\n",
       " 'd': array([0.23302494, 0.26576222, 0.92329008, 0.01753795, 0.54966465,\n",
       "        0.62221278, 0.37085458, 0.05321827, 0.18675867, 0.87818805,\n",
       "        0.3616857 , 0.84886774, 0.08076974, 0.85557681, 0.71760161,\n",
       "        0.49695615, 0.23777212, 0.7127281 , 0.12939015, 0.60372764,\n",
       "        0.05818357, 0.44025726, 0.66788303, 0.42333529, 0.87269427,\n",
       "        0.77808172, 0.63611593, 0.58142241, 0.3561951 , 0.53839356,\n",
       "        0.13119696, 0.06114425, 0.70134339, 0.37432878, 0.01565333,\n",
       "        0.70248966, 0.2818261 , 0.28463553, 0.44851051, 0.5388553 ,\n",
       "        0.48992475, 0.7822408 , 0.61147766, 0.74086037, 0.57496588,\n",
       "        0.14292354, 0.49343722, 0.14274424, 0.84597549, 0.60159599])}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plt.scatter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'d 数据')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176830f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter('a','d',data=data)\n",
    "plt.xlabel('a 数据')\n",
    "plt.ylabel('d 数据')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['b']=np.abs(data['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117cba1d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b4de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#散点图的样式\n",
    "plt.scatter('a','b',data=data, c='r', marker='>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117e44c18>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117cf7780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# c颜色 color\n",
    "plt.scatter('c','d',data=data,c='c',marker='>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 柱状图绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "names=['A类型','B类型','C类型']\n",
    "value=[1,10,100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1180c30b8>,\n",
       "  <matplotlib.axis.XTick at 0x1180b5a90>,\n",
       "  <matplotlib.axis.XTick at 0x118139e10>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118011438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(names)),value)\n",
    "plt.xticks(range(len(names)),names) #刻度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1171826d8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(names,value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x118139400>,\n",
       "  <matplotlib.axis.XTick at 0x118187a90>,\n",
       "  <matplotlib.axis.XTick at 0x1181a3cf8>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118139710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(names)),value)\n",
    "plt.xticks(range(len(names)),names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'离散数据散点图')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1181a8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(len(names)),value)\n",
    "plt.xticks(range(len(names)),names)\n",
    "plt.title('离散数据散点图')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 子图-SubPlot\n",
    "\n",
    "1.将一个画布进行切分(Figure)\n",
    "\n",
    "2.将切分后的图分配到固定的位置\n",
    "\n",
    "3.将图可以设置成固定的大小\n",
    "\n",
    "\n",
    "栅格系统，数字确定它的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'离散数据的柱状图、散点图、折线图')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1186eb4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(131)\n",
    "plt.bar(names,value,color='r')\n",
    "plt.subplot(236)\n",
    "plt.scatter(names,value,color='y')\n",
    "plt.subplot(333)\n",
    "plt.plot(names,value,color='g')\n",
    "plt.title('离散数据的柱状图、散点图、折线图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
